yangdx
Raphaël MANSUY
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3 changed files with 634 additions and 160 deletions
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env.example
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env.example
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### This is sample file of .env
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###############################################################################
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### ⚡️ QUICK START: OpenAI Configuration (Recommended)
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###############################################################################
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### To get started with OpenAI, you only need:
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### 1. Set your OpenAI API key (get from https://platform.openai.com/api-keys)
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### export OPENAI_API_KEY="sk-your-actual-api-key"
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### 2. Then you can start the server with default OpenAI configuration:
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### lightrag-server
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###
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### The default configuration will use:
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### - LLM: gpt-4o-mini (entity/relation extraction, graph merging, query answering)
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### - Embedding: text-embedding-3-small (vector embeddings)
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### No additional configuration needed!
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###
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### See LLM and Embedding Configuration sections below to customize models
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###############################################################################
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###########################
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### Server Configuration
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###########################
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@ -40,13 +23,13 @@ WEBUI_DESCRIPTION="Simple and Fast Graph Based RAG System"
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# WORKING_DIR=<absolute_path_for_working_dir>
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### Tiktoken cache directory (Store cached files in this folder for offline deployment)
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# TIKTOKEN_CACHE_DIR=./temp/tiktoken
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# TIKTOKEN_CACHE_DIR=/app/data/tiktoken
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### Ollama Emulating Model and Tag (used only when EMBEDDING_BINDING=ollama)
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### Ollama Emulating Model and Tag
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# OLLAMA_EMULATING_MODEL_NAME=lightrag
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OLLAMA_EMULATING_MODEL_TAG=latest
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### Max nodes return from grap retrieval in webui
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### Max nodes return from graph retrieval in webui
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# MAX_GRAPH_NODES=1000
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### Logging level
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@ -67,44 +50,30 @@ OLLAMA_EMULATING_MODEL_TAG=latest
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# JWT_ALGORITHM=HS256
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### API-Key to access LightRAG Server API
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### Use this key in HTTP requests with the 'X-API-Key' header
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### Example: curl -H "X-API-Key: your-secure-api-key-here" http://localhost:9621/query
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# LIGHTRAG_API_KEY=your-secure-api-key-here
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# WHITELIST_PATHS=/health,/api/*
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###############################################################################
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### OpenAI API Key (Required for OpenAI LLM and Embedding)
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### Get your key from: https://platform.openai.com/api-keys
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### This is the PRIMARY way to configure OpenAI (environment variable takes precedence)
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###############################################################################
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# OPENAI_API_KEY=sk-your-actual-openai-api-key-here
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######################################################################################
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### Query Configuration
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###
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### How to control the context lenght sent to LLM:
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### How to control the context length sent to LLM:
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### MAX_ENTITY_TOKENS + MAX_RELATION_TOKENS < MAX_TOTAL_TOKENS
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### Chunk_Tokens = MAX_TOTAL_TOKENS - Actual_Entity_Tokens - Actual_Reation_Tokens
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### Chunk_Tokens = MAX_TOTAL_TOKENS - Actual_Entity_Tokens - Actual_Relation_Tokens
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######################################################################################
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# LLM responde cache for query (Not valid for streaming response)
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# LLM response cache for query (Not valid for streaming response)
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ENABLE_LLM_CACHE=true
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# COSINE_THRESHOLD=0.2
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### Number of entities or relations retrieved from KG
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# TOP_K=40
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### Maxmium number or chunks for naive vector search
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### Maximum number or chunks for naive vector search
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# CHUNK_TOP_K=20
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### control the actual enties send to LLM
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### control the actual entities send to LLM
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# MAX_ENTITY_TOKENS=6000
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### control the actual relations send to LLM
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# MAX_RELATION_TOKENS=8000
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### control the maximum tokens send to LLM (include entities, raltions and chunks)
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### control the maximum tokens send to LLM (include entities, relations and chunks)
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# MAX_TOTAL_TOKENS=30000
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### maximum number of related chunks per source entity or relation
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### The chunk picker uses this value to determine the total number of chunks selected from KG(knowledge graph)
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### Higher values increase re-ranking time
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# RELATED_CHUNK_NUMBER=5
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### chunk selection strategies
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### VECTOR: Pick KG chunks by vector similarity, delivered chunks to the LLM aligning more closely with naive retrieval
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### WEIGHT: Pick KG chunks by entity and chunk weight, delivered more solely KG related chunks to the LLM
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@ -119,7 +88,7 @@ ENABLE_LLM_CACHE=true
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RERANK_BINDING=null
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### Enable rerank by default in query params when RERANK_BINDING is not null
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# RERANK_BY_DEFAULT=True
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### rerank score chunk filter(set to 0.0 to keep all chunks, 0.6 or above if LLM is not strong enought)
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### rerank score chunk filter(set to 0.0 to keep all chunks, 0.6 or above if LLM is not strong enough)
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# MIN_RERANK_SCORE=0.0
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### For local deployment with vLLM
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@ -157,7 +126,7 @@ SUMMARY_LANGUAGE=English
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# CHUNK_SIZE=1200
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# CHUNK_OVERLAP_SIZE=100
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### Number of summary semgments or tokens to trigger LLM summary on entity/relation merge (at least 3 is recommented)
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### Number of summary segments or tokens to trigger LLM summary on entity/relation merge (at least 3 is recommended)
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# FORCE_LLM_SUMMARY_ON_MERGE=8
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### Max description token size to trigger LLM summary
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# SUMMARY_MAX_TOKENS = 1200
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### Maximum context size sent to LLM for description summary
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# SUMMARY_CONTEXT_SIZE=12000
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### control the maximum chunk_ids stored in vector and graph db
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# MAX_SOURCE_IDS_PER_ENTITY=300
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# MAX_SOURCE_IDS_PER_RELATION=300
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### control chunk_ids limitation method: KEEP, FIFO (KEEP: Keep oldest, FIFO: First in first out)
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# SOURCE_IDS_LIMIT_METHOD=KEEP
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### Maximum number of file paths stored in entity/relation file_path field
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# MAX_FILE_PATHS=30
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### maximum number of related chunks per source entity or relation
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### The chunk picker uses this value to determine the total number of chunks selected from KG(knowledge graph)
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### Higher values increase re-ranking time
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# RELATED_CHUNK_NUMBER=5
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###############################
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### Concurrency Configuration
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###############################
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@ -185,17 +167,10 @@ MAX_PARALLEL_INSERT=2
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### LLM request timeout setting for all llm (0 means no timeout for Ollma)
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# LLM_TIMEOUT=180
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# PRIMARY CONFIGURATION: OpenAI (Recommended for production)
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LLM_BINDING=openai
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LLM_MODEL=gpt-4o-mini
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LLM_MODEL=gpt-4o
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LLM_BINDING_HOST=https://api.openai.com/v1
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LLM_BINDING_API_KEY=your_api_key
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# Note: By default, uses OPENAI_API_KEY environment variable
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### ALTERNATIVE: Using gpt-4o for higher quality (higher cost)
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# LLM_BINDING=openai
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# LLM_MODEL=gpt-4o
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# LLM_BINDING_HOST=https://api.openai.com/v1
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### Optional for Azure
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# AZURE_OPENAI_API_VERSION=2024-08-01-preview
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# OPENAI_LLM_TEMPERATURE=0.9
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### Set the max_tokens to mitigate endless output of some LLM (less than LLM_TIMEOUT * llm_output_tokens/second, i.e. 9000 = 180s * 50 tokens/s)
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### Typically, max_tokens does not include prompt content, though some models, such as Gemini Models, are exceptions
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### For vLLM/SGLang doployed models, or most of OpenAI compatible API provider
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### For vLLM/SGLang deployed models, or most of OpenAI compatible API provider
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# OPENAI_LLM_MAX_TOKENS=9000
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### For OpenAI o1-mini or newer modles
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OPENAI_LLM_MAX_COMPLETION_TOKENS=9000
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# OPENAI_LLM_REASONING_EFFORT=minimal
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### OpenRouter Specific Parameters
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# OPENAI_LLM_EXTRA_BODY='{"reasoning": {"enabled": false}}'
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### Qwen3 Specific Parameters depoly by vLLM
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### Qwen3 Specific Parameters deploy by vLLM
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# OPENAI_LLM_EXTRA_BODY='{"chat_template_kwargs": {"enable_thinking": false}}'
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### use the following command to see all support options for Ollama LLM
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@ -244,52 +219,44 @@ OLLAMA_LLM_NUM_CTX=32768
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####################################################################################
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### Embedding Configuration (Should not be changed after the first file processed)
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### EMBEDDING_BINDING: openai, ollama, azure_openai, jina, lollms, aws_bedrock
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### PRIMARY CONFIGURATION: OpenAI (Recommended)
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### EMBEDDING_BINDING: ollama, openai, azure_openai, jina, lollms, aws_bedrock
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####################################################################################
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# EMBEDDING_TIMEOUT=30
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EMBEDDING_BINDING=openai
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EMBEDDING_MODEL=text-embedding-3-small
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EMBEDDING_DIM=1536
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EMBEDDING_BINDING_HOST=https://api.openai.com/v1
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# EMBEDDING_BINDING_API_KEY=your_openai_api_key (uses OPENAI_API_KEY env var by default)
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EMBEDDING_BINDING=ollama
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EMBEDDING_MODEL=bge-m3:latest
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EMBEDDING_DIM=1024
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EMBEDDING_BINDING_API_KEY=your_api_key
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# If the embedding service is deployed within the same Docker stack, use host.docker.internal instead of localhost
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EMBEDDING_BINDING_HOST=http://localhost:11434
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### ALTERNATIVE: Text-embedding-3-large (higher quality, higher cost)
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### OpenAI compatible (VoyageAI embedding openai compatible)
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# EMBEDDING_BINDING=openai
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# EMBEDDING_MODEL=text-embedding-3-large
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# EMBEDDING_DIM=3072
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# EMBEDDING_BINDING_HOST=https://api.openai.com/v1
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### ALTERNATIVE: Local Ollama embedding (no API key required, requires Ollama service)
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# EMBEDDING_BINDING=ollama
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# EMBEDDING_MODEL=bge-m3:latest
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# EMBEDDING_DIM=1024
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# EMBEDDING_BINDING_HOST=http://localhost:11434
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# EMBEDDING_BINDING_API_KEY=your_api_key
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# If the embedding service is deployed within the same Docker stack, use host.docker.internal instead of localhost
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### ALTERNATIVE: Azure OpenAI embedding
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# EMBEDDING_BINDING=azure_openai
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# AZURE_EMBEDDING_DEPLOYMENT=text-embedding-3-small
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### Optional for Azure
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# AZURE_EMBEDDING_DEPLOYMENT=text-embedding-3-large
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# AZURE_EMBEDDING_API_VERSION=2023-05-15
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# AZURE_EMBEDDING_ENDPOINT=your_endpoint
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# AZURE_EMBEDDING_API_KEY=your_api_key
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### ALTERNATIVE: Jina AI Embedding
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### Jina AI Embedding
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# EMBEDDING_BINDING=jina
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# EMBEDDING_BINDING_HOST=https://api.jina.ai/v1/embeddings
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# EMBEDDING_MODEL=jina-embeddings-v4
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# EMBEDDING_DIM=2048
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# EMBEDDING_BINDING_API_KEY=your_api_key
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### Ollama embedding options (only used when EMBEDDING_BINDING=ollama)
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### Optional for Ollama embedding
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OLLAMA_EMBEDDING_NUM_CTX=8192
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### use the following command to see all support options for Ollama embedding
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### lightrag-server --embedding-binding ollama --help
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####################################################################
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### WORKSPACE setting workspace name for all storage types
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### in the purpose of isolating data from LightRAG instances.
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### WORKSPACE sets workspace name for all storage types
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### for the purpose of isolating data from LightRAG instances.
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### Valid workspace name constraints: a-z, A-Z, 0-9, and _
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####################################################################
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# WORKSPACE=space1
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@ -57,8 +57,24 @@ DEFAULT_HISTORY_TURNS = 0
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DEFAULT_MIN_RERANK_SCORE = 0.0
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DEFAULT_RERANK_BINDING = "null"
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# File path configuration for vector and graph database(Should not be changed, used in Milvus Schema)
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# Default source ids limit in meta data for entity and relation
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DEFAULT_MAX_SOURCE_IDS_PER_ENTITY = 3
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DEFAULT_MAX_SOURCE_IDS_PER_RELATION = 3
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SOURCE_IDS_LIMIT_METHOD_KEEP = "KEEP" # Keep oldest
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SOURCE_IDS_LIMIT_METHOD_FIFO = "FIFO" # First In First Out (Keep newest)
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DEFAULT_SOURCE_IDS_LIMIT_METHOD = SOURCE_IDS_LIMIT_METHOD_KEEP
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VALID_SOURCE_IDS_LIMIT_METHODS = {
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SOURCE_IDS_LIMIT_METHOD_KEEP,
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SOURCE_IDS_LIMIT_METHOD_FIFO,
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}
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# Default file_path limit in meta data for entity and relation (Use same limit method as source_ids)
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DEFAULT_MAX_FILE_PATHS = 2
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# Field length of file_path in Milvus Schema for entity and relation (Should not be changed)
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# file_path must store all file paths up to the DEFAULT_MAX_FILE_PATHS limit within the metadata.
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DEFAULT_MAX_FILE_PATH_LENGTH = 32768
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# Placeholder for more file paths in meta data for entity and relation (Should not be changed)
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DEFAULT_FILE_PATH_MORE_PLACEHOLDER = "truncated"
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# Default temperature for LLM
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DEFAULT_TEMPERATURE = 1.0
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